The long term objective is to enhance the quality of care and cost-effectiveness of care provided to preterm infants in the neonatal intensive care unit (NICU) by providing health care practitioners with an automated knowledge-based system designed to augment their practice. The aim is to ascertain the core features of decision support environment for a nontrivial set of patient problems in patients cared for by a variety of practitioners with convergent and divergent values and subjective probabilities for decision making in a clinical setting. These activities are aimed at developing a prototype decision support system (DSS) for use by neonatal nurse practitioners (NNP)s and neonatologists in the NICU. The problem of determination of readiness for oral feeding in the preterm infant will provide the initial clinical focus for this research. Ethnography and the Critical Decision Method will be used to elucidate practitioners subjective estimates of how the determination of readiness for oral feedings in the preterm infant is made. A content analysis will be used to validate and, if applicable, to add new decision strategies to the model. Bayesian hierarchical analyses will be used for the uncertainty phase. A Multiattribute Utility Technique (MAUT) will be used to conduct preference analyses. Once the decision model has been structured, validity testing will be performed by a national panel of experts. Next, a prototype Decision Support System (DSS) system will be built. The decision-making model and the accompanying DSS will be tested for accuracy, efficiency and user acceptance at the initial research site. The decision-making model and DSS will be field tested for generalizability in a number of different hospitals. Finally, use of the process for a second clinical problem will be undertaken to test not only generalizability as it relates to a clinical problems, but also can help determine which elements of the decision modeling process can be generalized to other areas of nursing practice. A future goal will be to compare practice using traditional techniques and practice augmented using a decision-analytic model incorporated into a DSS to discern the effect on patient outcomes and resource use.